ZHANG Qin, GU Yu, XU Ying, et al. Feature selection for SAR images using the hybrid intelligent optimization algorithm[J]. Journal of Remote Sensing, 2016,20(1):73-79.
ZHANG Qin, GU Yu, XU Ying, et al. Feature selection for SAR images using the hybrid intelligent optimization algorithm[J]. Journal of Remote Sensing, 2016,20(1):73-79. DOI: 10.11834/jrs.20165140.
To improve the automatic target recognition accuracy of SAR images and real-time performance
this study proposes a feature selection algorithm based on hybrid intelligent optimization for such images. First
a fractal feature is used to enhance an SAR image. An azimuth estimation method is then developed based on the image moment after image segmentation. Subsequently
the features of Zernike moment
Gabor wavelet coefficients
and gray level co-occurrence matrix are extracted from the original and the rectified images to form feature candidates. The genetic algorithm and the binary particle swarm optimization algorithm are combined to select features for SAR images. The effectiveness of the proposed algorithm is verified with the MSTAR database. Results demonstrate that the optimal feature sets can be generalized
thereby improving the target recognition rate and reducing recognition time.
关键词
SAR图像特征选择混合智能优化算法分形特征Zernike矩
Keywords
SAR imagefeature selectionhybrid intelligent optimization algorithmfractal featureZernike moment